Phys. Rev. B 100, 214108 (2019) - Thermodynamic integration by neural network potentials based on first-principles dynamic calculations

Thermodynamic integration by neural network potentials based on first-principles dynamic calculations

Shogo Fukushima, Eisaku Ushijima, Hiroyuki Kumazoe, Akihide Koura, Fuyuki Shimojo, Kohei Shimamura, Masaaki Misawa, Rajiv K. Kalia, Aiichiro Nakano, and Priya Vashishta

Phys. Rev. B 100, 214108 – Published 9 December 2019

Abstract

Simulation-size effect in evaluating the melting temperature of material is studied systematically by combining thermodynamic integration (TI) based on first-principles molecular-dynamics (FPMD) simulations and machine learning. Since the numerical integration to determine the free energies of two different phases as a function of temperature is very time consuming, the FPMD-based TI method has only been applied to small systems, i.e., less than 100 atoms. To accelerate the numerical integration, we here construct an interatomic potential based on the artificial neural-network (ANN) method, which retains the first-principles accuracy at a significantly lower computational cost. The free energies of the solid and liquid phases of rubidium are accurately obtained by the ANN potential, where its weight parameters are optimized to reproduce FPMD results. The ANN results reveal a significant size dependence up to 500 atoms.

Received 6 April 2019
Revised 17 October 2019

DOI:https://doi.org/10.1103/PhysRevB.100.214108

©2019 American Physical Society

Physics Subject Headings (PhySH)

Phase diagrams Thermodynamics

Artificial neural networks First-principles calculations Neuroimaging

Interdisciplinary PhysicsStatistical Physics & ThermodynamicsCondensed Matter, Materials & Applied Physics

Authors & Affiliations

Shogo Fukushima, Eisaku Ushijima, Hiroyuki Kumazoe, Akihide Koura, and Fuyuki Shimojo

Department of Physics, Kumamoto University, Kumamoto 860–8555, Japan

Kohei Shimamura

Graduate School of System Informatics, Kobe University, Kobe 657–8501, Japan

Faculty of Science and Engineering, Kyushu Sangyo University, Fukuoka 813–8503, Japan

Rajiv K. Kalia, Aiichiro Nakano , and Priya Vashishta

Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, USA

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Issue

Vol. 100, Iss. 21 — 1 December 2019

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